Re-analysis of GSE21933 dataset: Understanding Phalanx Human OneArray Data
Spandan Pandya
2025-01-24
About the data:
Series ID: GSE21933 Platform: Phalanx Human OneAray v5 GPL6254
Experiment type: Expression profiling by Array Details:
Phalanx Biotech Group’s Human OneArray v5 contains 32,048 features, 30968 detection probes and 1080 control probes, spotted onto glass slides using a proprietary non-contact printing method. Detection probes are annotated against the human genome and grouped into the following categories:
Group 1 - gene specific: exon
Group 2 - intron hit
Group 3 - intergenic
Group 4 - multi-gene hits
Group 5 - no hit to genome
Group 6 - >200hits to genome (Mostly represents control sets)
Lo FY, Chang JW, Chang IS, Chen YJ et al. The database of chromosome imbalance regions and genes resided in lung cancer from Asian and Caucasian identified by array-comparative genomic hybridization. BMC Cancer 2012 Jun 12;12:235. PMID: 22691236
Data and Normalization
data_id <- "GSE21933"
eset <-getGEO(data_id) #get dataset## Found 1 file(s)## GSE21933_series_matrix.txt.gzgse <- eset[[1]] #get gene dataset
# head(pData(gse)) #phenotype data
# head(fData(gse)) #feature data
# head(exprs(gse)) #complete expression data set
summary(exprs(gse)) #statistical analysis of all summary()## GSM545615 GSM545616 GSM545617 GSM545618
## Min. : 0.1536 Min. : 0.4077 Min. : 0.04938 Min. : 0.5312
## 1st Qu.: 3.8381 1st Qu.: 3.8453 1st Qu.: 3.84864 1st Qu.: 3.8799
## Median : 5.8976 Median : 5.8950 Median : 5.89495 Median : 5.8941
## Mean : 6.2761 Mean : 6.2757 Mean : 6.27665 Mean : 6.2752
## 3rd Qu.: 8.4292 3rd Qu.: 8.4269 3rd Qu.: 8.42835 3rd Qu.: 8.4281
## Max. :15.9989 Max. :15.9989 Max. :15.99895 Max. :15.9989
## GSM545619 GSM545620 GSM545621 GSM545622
## Min. : 0.283 Min. : 0.3436 Min. : 0.1726 Min. : 0.01868
## 1st Qu.: 3.819 1st Qu.: 3.8353 1st Qu.: 3.8568 1st Qu.: 3.84881
## Median : 5.894 Median : 5.8965 Median : 5.8949 Median : 5.89586
## Mean : 6.277 Mean : 6.2760 Mean : 6.2766 Mean : 6.27655
## 3rd Qu.: 8.430 3rd Qu.: 8.4286 3rd Qu.: 8.4292 3rd Qu.: 8.42895
## Max. :15.999 Max. :15.9989 Max. :15.9989 Max. :15.99893
## GSM545623 GSM545624 GSM545625 GSM545626
## Min. : 0.2089 Min. : 0.2229 Min. : 0.008928 Min. : 0.039
## 1st Qu.: 3.8365 1st Qu.: 3.8332 1st Qu.: 3.852787 1st Qu.: 3.845
## Median : 5.8907 Median : 5.8982 Median : 5.895908 Median : 5.896
## Mean : 6.2757 Mean : 6.2759 Mean : 6.275721 Mean : 6.276
## 3rd Qu.: 8.4298 3rd Qu.: 8.4313 3rd Qu.: 8.429130 3rd Qu.: 8.428
## Max. :15.9989 Max. :15.9989 Max. :15.998947 Max. :15.999
## GSM545627 GSM545628 GSM545629 GSM545630
## Min. : 0.0598 Min. : 0.01137 Min. : 0.2113 Min. : 0.1679
## 1st Qu.: 3.8114 1st Qu.: 3.82077 1st Qu.: 3.8270 1st Qu.: 3.8655
## Median : 5.9008 Median : 5.89057 Median : 5.8864 Median : 5.8968
## Mean : 6.2769 Mean : 6.27676 Mean : 6.2747 Mean : 6.2761
## 3rd Qu.: 8.4302 3rd Qu.: 8.43115 3rd Qu.: 8.4314 3rd Qu.: 8.4304
## Max. :15.9989 Max. :15.99893 Max. :15.9989 Max. :15.9989
## GSM545631 GSM545632 GSM545633 GSM545634
## Min. : 0.02635 Min. : 0.260 Min. : 0.05111 Min. : 0.009472
## 1st Qu.: 3.83963 1st Qu.: 3.867 1st Qu.: 3.85254 1st Qu.: 3.840950
## Median : 5.89913 Median : 5.900 Median : 5.89552 Median : 5.894485
## Mean : 6.27604 Mean : 6.275 Mean : 6.27769 Mean : 6.277425
## 3rd Qu.: 8.42879 3rd Qu.: 8.428 3rd Qu.: 8.43166 3rd Qu.: 8.430225
## Max. :15.99893 Max. :15.999 Max. :15.99890 Max. :15.998947
## GSM545635 GSM545636 GSM545637 GSM545638
## Min. : 0.000 Min. : 0.523 Min. : 0.3539 Min. : 0.3566
## 1st Qu.: 3.844 1st Qu.: 3.834 1st Qu.: 3.8428 1st Qu.: 3.8392
## Median : 5.894 Median : 5.889 Median : 5.9023 Median : 5.8975
## Mean : 6.277 Mean : 6.273 Mean : 6.2740 Mean : 6.2766
## 3rd Qu.: 8.429 3rd Qu.: 8.427 3rd Qu.: 8.4290 3rd Qu.: 8.4297
## Max. :15.999 Max. :15.999 Max. :15.9989 Max. :15.9989
## GSM545639 GSM545640 GSM545641 GSM545642
## Min. : 0.2665 Min. : 0.08381 Min. : 0.4029 Min. : 0.05743
## 1st Qu.: 3.8352 1st Qu.: 3.87108 1st Qu.: 3.8324 1st Qu.: 3.83935
## Median : 5.8931 Median : 5.89739 Median : 5.8930 Median : 5.89279
## Mean : 6.2754 Mean : 6.27513 Mean : 6.2761 Mean : 6.27641
## 3rd Qu.: 8.4288 3rd Qu.: 8.43237 3rd Qu.: 8.4306 3rd Qu.: 8.43054
## Max. :15.9989 Max. :15.99895 Max. :15.9989 Max. :15.99893
## GSM545643 GSM545644 GSM545645 GSM545646
## Min. : 0.3071 Min. : 0.4294 Min. : 0.03132 Min. : 0.3196
## 1st Qu.: 3.8600 1st Qu.: 3.8814 1st Qu.: 3.84646 1st Qu.: 3.8684
## Median : 5.8883 Median : 5.8968 Median : 5.89639 Median : 5.9005
## Mean : 6.2759 Mean : 6.2753 Mean : 6.27705 Mean : 6.2748
## 3rd Qu.: 8.4299 3rd Qu.: 8.4290 3rd Qu.: 8.42751 3rd Qu.: 8.4301
## Max. :15.9989 Max. :15.9989 Max. :15.99895 Max. :15.9989
## GSM545647 GSM545648 GSM545649 GSM545650
## Min. : 0.1531 Min. : 0.008351 Min. : 0.1819 Min. : 0.3828
## 1st Qu.: 3.8076 1st Qu.: 3.839472 1st Qu.: 3.8272 1st Qu.: 3.8240
## Median : 5.8911 Median : 5.896806 Median : 5.8958 Median : 5.8992
## Mean : 6.2754 Mean : 6.277289 Mean : 6.2767 Mean : 6.2751
## 3rd Qu.: 8.4308 3rd Qu.: 8.430428 3rd Qu.: 8.4304 3rd Qu.: 8.4293
## Max. :15.9989 Max. :15.998947 Max. :15.9989 Max. :15.9989
## GSM545651 GSM545652 GSM545653 GSM545654
## Min. : 0.1291 Min. : 0.07773 Min. : 0.3255 Min. : 0.01947
## 1st Qu.: 3.8631 1st Qu.: 3.84617 1st Qu.: 3.8466 1st Qu.: 3.85324
## Median : 5.8989 Median : 5.89233 Median : 5.8969 Median : 5.89586
## Mean : 6.2770 Mean : 6.27683 Mean : 6.2754 Mean : 6.27742
## 3rd Qu.: 8.4321 3rd Qu.: 8.43029 3rd Qu.: 8.4304 3rd Qu.: 8.43093
## Max. :15.9989 Max. :15.99895 Max. :15.9989 Max. :15.99893
## GSM545655 GSM545656
## Min. : 0.005867 Min. : 0.04854
## 1st Qu.: 3.838326 1st Qu.: 3.83994
## Median : 5.897306 Median : 5.89339
## Mean : 6.276177 Mean : 6.27702
## 3rd Qu.: 8.429420 3rd Qu.: 8.42844
## Max. :15.998928 Max. :15.99893boxplot(exprs(gse), outline = FALSE, col = "gold") #Visualize the normalization
The Phenotype Data contains 42 samples and 40 features for the samples. The Feature Data: The feature data contains 30967 probes and 10 features for each samples.
Looking at the summary of expressions, the values for each sample falls between 0-16. Suggesting that these values a log2 normalized. Further, the figure below shows a boxplot of all the gene count values for each sample. As seen below, all the samples have lined up unifromly suggesting that the data is already log normalized.
Inspecting clinical Data
There are several columns within the metadata many of these are
repeating. The most important of these columns are the column-1 -
title, and columns 36 to 40
age:ch1,histology:ch1,sex:ch1,stage:ch1,tissue:ch1.
Further, most of the column names have a “:ch1” at the end. This was
removed. Lastly, the histology and stage columns have no data for
negative samples. This was changed to “neg”.
#Preparing sample metadata
samplesinfo <- pData(gse) #gettign sample data
samplesinfo <- samplesinfo[,c(1,36:40)] #There are multiple columns for same data, acquiring relevant metadata information.
colnames(samplesinfo) <- gsub(":ch1","",colnames(samplesinfo))
samplesinfo <- samplesinfo%>%
mutate(across(c(3,5),~replace_na(.,"Neg")))
samplesinfo <- samplesinfo%>%
mutate_all(~gsub(" years","",.))%>%
mutate(across(c(histology,sex,stage,tissue),factor))%>%
mutate(age = as.numeric(age))%>%
mutate(tissue = ifelse(tissue == "primary normal lung tissue","normal","tumor"))
samplesinfo$histology <- relevel(samplesinfo$histology, ref = "Neg")
samplesinfo$stage <- relevel(samplesinfo$stage, ref = "Neg")
samplesinfo$tissue <- relevel(as.factor(samplesinfo$tissue), ref = "normal")
#Prepare Features Metadata
featuresinfo <- fData(gse)
#Prepare expression data
exprs_data <- exprs(gse)
samplesinfo%>%
group_by(tissue, stage,histology)%>%
tally()%>%
spread(histology,n)The data set represents 42 lung tissue samples, 21 primary normal lung tissues and 21 primary lung tumor tissues. The tumor tissues are representing six stages of lung cancer IA,IB,IIB,IIIA,IIIB,IV. Further two hematological patterns are also represented in tumor samples, these are: adenocarcinoma (AD) and Squamous cell carcinoma (SQ).
Clustering and PCA analysis
corMatrix <- cor(exprs_data, use = "c")
rownames(samplesinfo) <- colnames(corMatrix)
pheatmap(corMatrix, annotation_col =samplesinfo[,3:6], annotation_row = samplesinfo[,3:6], cluster_rows = T, cluster_cols = T)
#Hierarchical cluster based dimension analysis
htree <- hclust(dist(t(exprs_data)), method = "average")
plot(htree) #It seems there is a specific pattern of clustering within samples. And this might be biologically relevant difference so I think it is better to makes sure we check with PCA,the variance cotnribution. 
pca_gse <- prcomp(t(exprs_data)) # Run PCA analysis
screeplot(pca_gse, npcs=min(10,length(pca_gse$sdev)),type = c("barplot","lines")) #Screeplots of all the PC;s and their cotnribution. 
# PCA plot by tissue
p1 <- cbind(samplesinfo, pca_gse$x)%>%
ggplot(aes(x= PC1, y = PC2, col=tissue, label = paste(tissue)))+
geom_point(size=5)+
geom_text_repel()
p2 <- cbind(samplesinfo, pca_gse$x)%>%
ggplot(aes(x= PC1, y = PC2, col=stage, label = paste(stage)))+
geom_point()+
geom_text_repel()
p3 <- cbind(samplesinfo, pca_gse$x)%>%
ggplot(aes(x= PC1, y = PC2, col=histology, label = paste(histology)))+
geom_point()+
geom_text_repel()
p4 <- cbind(samplesinfo, pca_gse$x)%>%
ggplot(aes(x= PC1, y = PC2, col=title, label = paste(title)))+
geom_point(size = 5)+
geom_text_repel(size = 5)+
ggsci::scale_fill_nejm()
ggarrange(p1,p2,p3,p4, ncol = 2, nrow = 2, common.legend = TRUE, legend = "bottom")
Filter dataset:
First I am filtering out only genes that belong to group-6 in the array as that represents the control genes. Lowly expressed genes must be filtered out as they can deviate the results of gene expression. The cut off for low expression is median gene expression of each sample. Next, I am applying three seperate strategies: 1) All samples have a data 2) At least 50% samples have all the data 3) At least 25% of sample have a data. However, this is a microarray dataset.
Exploring cut offs:
control_list <- rownames(gse@featureData[which(gse@featureData@data$GROUP ==6)]) #create a vector list of controls
control_matrix <- exprs_data[rownames(exprs_data)%in% control_list,] #isolate a matrix of just controls
mean_control <- colMeans(control_matrix)
mean_control <- data.frame(mean_control)%>%
rownames_to_column(var = "control")
colnames(mean_control) <- c("controls","mean")
cat(paste0("Mean of Controls= ",mean(control_matrix),"\n","Mean of data= ",mean(exprs_data),"\n", "Median of Controls= ",median(control_matrix),"\n","Median of data= ",median(exprs_data)))## Mean of Controls= 8.31688252769473
## Mean of data= 6.27604254788615
## Median of Controls= 7.96752265
## Median of data= 5.894992cutoff <- median(exprs_data) #take median of expression dataset
is_expressed <- exprs_data>cutoff
keep_all<- rowSums(is_expressed) >=42 #stringent should be present in all samples.
keep_50 <- rowSums(is_expressed)>=21 #should be present in at leasst 50% samples.
keep <- rowSums(is_expressed)<10 # lenient threshold present in at least 25% samples
gse_filt <- rbind(table(keep),table(keep_50),table(keep_all))
rownames(gse_filt) <- c("25%","50%","100%")
gse_filt## FALSE TRUE
## 25% 17877 13090
## 50% 15596 15371
## 100% 22070 8897gse_25 <- gse[keep,]
gse_50 <- gse[keep_50,]
gse_100 <- gse[keep_all,]
dim_table <- data.frame(SampleCount = c("25%","50%","100%"),
features = c(dim(gse_25)[1],dim(gse_50)[1],dim(gse_100)[1]),
features = c(dim(gse_25)[2],dim(gse_50)[2],dim(gse_100)[2]))
print(dim_table)## SampleCount features features.1
## 1 25% 13090 42
## 2 50% 15371 42
## 3 100% 8897 42plot(density(control_matrix), main = "Expression Density", xlab = "log2 intensity",)
abline(v = c(1,6), col = c("red","blue"), lty = 2) # typical cutoff
Filter genes:
#Filter only sampels that are group-1 or known genes
exon_genes <- row.names(featuresinfo[which(featuresinfo$GROUP == 1),])
gse[exon_genes,]## ExpressionSet (storageMode: lockedEnvironment)
## assayData: 19677 features, 42 samples
## element names: exprs
## protocolData: none
## phenoData
## sampleNames: GSM545615 GSM545616 ... GSM545656 (42 total)
## varLabels: title geo_accession ... tissue:ch1 (40 total)
## varMetadata: labelDescription
## featureData
## featureNames: PH_hs_0000002 PH_hs_0000004 ... PH_hs_0041207 (19677
## total)
## fvarLabels: ID GROUP ... ORF (10 total)
## fvarMetadata: Column Description labelDescription
## experimentData: use 'experimentData(object)'
## pubMedIds: 22691236
## Annotation: GPL6254exprs_data <- exprs_data[exon_genes,]
universe_symbol <- featuresinfo[exon_genes, "GENE_SYMBOL"]
universe_ensemble <- featuresinfo[exon_genes, "ENSEMBL_GENE_ID"]
# Determine final name using the priority rules
universe_names <- character(length(exon_genes))
for (i in seq_along(exon_genes)) {
if (is_single_valid(universe_symbol[i])) {
universe_names[i] <- universe_symbol[i]
} else if (is_single_valid(universe_ensemble[i])) {
universe_names[i] <- universe_ensemble[i]
} else {
universe_names[i] <- exon_genes[i] # fallback to original name
}
}
# Write final names to output
write_lines(universe_names, file = "../outputs/universe_WGCNA.txt")Differential Expression
Combined effect and interaction effect
set.seed(1234)
design_combined <- model.matrix(~0+tissue+ histology+stage, data = samplesinfo)
design_interaction <- model.matrix(~tissue*histology*stage, data = samplesinfo)AD versus SQ versus neg
design_gse <- model.matrix(~0+samplesinfo$histology)
colnames(design_gse)<-c("neg","AD","SQ")
fit <- lmFit(exprs(gse_100),design_gse) #fit all the genes
contrasts_gse <- makeContrasts( ADvsneg = AD-neg, #AD versus negative
SQvsneg = SQ-neg, #SQ versus negative
ADvsSQ = AD-SQ, #AD versus SQ
SQvsAD = SQ-AD, #SQ versus AD
avsavg = (AD+SQ)/2-neg, #negative versus combined disease how significantly different SQ and AD are from negative.
ADvsSQtoneg = ((AD-neg)-(SQ-neg)), #Checks if AD is significantly more different than SQ
levels =design_gse)
cont_names <- colnames(contrasts_gse)
result_all <- data.frame()
for (i in cont_names){
fit_contrast <- contrasts.fit(fit, contrasts_gse[,i])
fit_contrast <- eBayes(fit_contrast)
results <- topTable(fit_contrast, adjust="fdr", number=Inf)
results$Genes <- rownames(results)
results$Contrasts<- i
result_all <- bind_rows(result_all,results)
plot <-ggplot(results, aes(x = logFC, y = -log10(adj.P.Val))) +
geom_point(aes(color = adj.P.Val < 0.05), alpha = 0.5) +
scale_color_manual(values = c("black", "red")) +
labs(title = paste("Volcano Plot:", i), x = "Log2 Fold Change", y = "-Log10 Adjusted P-value") +
theme_minimal()
print(plot)
ggsave(filename = paste0(i,"_volcano.png"), path = "../figures")
}
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image
## Saving 7 x 5 in image#volcano plot
ggplot(result_all, aes(x = logFC, y = -log10(adj.P.Val), color = Contrasts)) +
geom_point(alpha = 0.5) +
labs(title = "Volcano Plot for Multiple Contrasts", x = "Log2 Fold Change", y = "-Log10 Adjusted P-value") +
theme_minimal()
ggsave(filename = "volcano_all_contrasts.png",path = "../figures")## Saving 7 x 5 in imageWGCNA
Chose Threshold
I chose the threshold to be at 15 as this had a mean connectivity of at scale free topology index of
softPower <- 15
cat("The soft power is",softPower)## The soft power is 15adj_matrix <- t(exprs_data) #filtered genes present in all samples
adjacency_mat <- adjacency(adj_matrix, power = softPower)
# Turn adjacency into topological overlap
TOM <- TOMsimilarity(adjacency_mat)## ..connectivity..
## ..matrix multiplication (system BLAS)..
## ..normalization..
## ..done.dissTOM <- 1 - TOM
# Hierarchical clustering
geneTree <- hclust(as.dist(dissTOM), method = "average")
plot(geneTree, main = "Gene clustering")
# Module identification using dynamic tree cut deepsplit allows adjusting granulatity.
dynamicMods <- cutreeDynamic(dendro = geneTree, distM = dissTOM,
deepSplit = 2, pamRespectsDendro = FALSE,
minClusterSize = 100) #minClustersize seems optimal with at least 100 genes per cluster. ## ..cutHeight not given, setting it to 0.999 ===> 99% of the (truncated) height range in dendro.
## ..done.cat("DYnamic tree with modules:")## DYnamic tree with modules:table(dynamicMods)## dynamicMods
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 6965 1974 1621 1580 1263 1199 878 826 767 601 594 518 430 337 124# Assign module colors
dynamicColors <- labels2colors(dynamicMods)
plotDendroAndColors(geneTree, dynamicColors, "Dynamic Tree Cut",
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05)
cat("DYnamic tree modules with colors:")## DYnamic tree modules with colors:table(dynamicColors)## dynamicColors
## black blue brown cyan green greenyellow
## 826 1621 1580 124 1199 518
## grey magenta pink purple red salmon
## 6965 601 767 594 878 337
## tan turquoise yellow
## 430 1974 1263# STEP-4: Relate Modules to Traits
# Calculate eigengenes
MEs <- moduleEigengenes(adj_matrix, colors = dynamicColors)$eigengenes
rowname_MEs <- row.names(MEs)
MEs <- orderMEs(MEs)
row.names(MEs) <- rowname_MEs
# Correlate eigengenes with traits
traits <- samplesinfo %>%
mutate(across(where(is.factor), ~as.numeric(as.factor(.)))) %>%
select(-c("title","sex")) # Remove non-trait columns like ID if present
#traits <- model.matrix(~ histology + sex + stage + tissue + age, data = samplesinfo)[,-1]
moduleTraitCor <- cor(MEs,traits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples = ncol(adj_matrix))
all(rownames(MEs) == rownames(traits)) # must be TRUE## [1] TRUE# Plot correlation heatmap
# Load required libraries
# Prepare correlation and p-value matrices
moduleTraitCor <- cor(MEs, traits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples = ncol(adj_matrix))
# Melt into long format for ggplot
df_cor <- melt(moduleTraitCor)
df_pval <- melt(moduleTraitPvalue)
# Combine and format labels
df_combined <- df_cor
df_combined$Pvalue <- df_pval$value
df_combined$Label <- paste0(signif(df_cor$value, 2), "\n(",
signif(df_pval$value, 1), ")")
# Optional: set factor levels to preserve order
df_combined$Var1 <- factor(df_combined$Var1, levels = rownames(moduleTraitCor))
df_combined$Var2 <- factor(df_combined$Var2, levels = colnames(moduleTraitCor))
# Plot
ggplot(df_combined, aes(x = Var2, y = Var1, fill = value)) +
geom_tile(color = "grey90") +
geom_text(aes(label = Label), size = 3.5) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
name = "Correlation") +
theme_minimal(base_size = 14) +
labs(x = "Traits", y = "Modules", title = "Module-Trait Relationships") +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid = element_blank(),
plot.title = element_text(hjust = 0.5))
ggsave(filename = "WGCNA_ModuleCorr.png",path = "../figures")## Saving 12 x 9 in imagenames(dynamicColors) <- colnames(adj_matrix)
kWithin <- intramodularConnectivity(adjacency_mat, dynamicColors) # Calculate intra-modular connectivity (hubness)
for (m in unique(dynamicColors)) {
module_genes <- names(dynamicColors)[which(dynamicColors == m)]
if (length(module_genes) == 0) next
# Order genes by intramodular connectivity
hub_genes <- module_genes[order(kWithin[module_genes, "kWithin"], decreasing = TRUE)]
# Extract columns
gene_symbol <- featuresinfo[hub_genes, "GENE_SYMBOL"]
ensembl_id <- featuresinfo[hub_genes, "ENSEMBL_GENE_ID"]
# Determine final name using the priority rules
final_names <- character(length(hub_genes))
for (i in seq_along(hub_genes)) {
if (is_single_valid(gene_symbol[i])) {
final_names[i] <- gene_symbol[i]
} else if (is_single_valid(ensembl_id[i])) {
final_names[i] <- ensembl_id[i]
} else {
final_names[i] <- hub_genes[i] # fallback to original name
}
}
# Write final names to output
write_lines(final_names, file = paste0("../outputs/hub_", m, ".txt"))
}